English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We take up optimality results for robust Kalman filtering fromRuckdeschel[2001,2010] where robustness is understood in a distributionalsense, i.e.; we enlarge the distribution assumptions made in the ideal model bysuitable neighborhoods, allowing for outliers which in our context may besystem-endogenous/propagating or -exogenous/non-propagating, inducing thesomewhat conflicting goals of tracking and attenuation. Correspondingly, thecited references provide optimally-robust procedures to deal with each type ofoutliers separately, but in case of IO-robustness does not say much about theimplementation. We discuss this in more detail in this paper. Most importantly,we define a hybrid filter combining AO- and IO-optimal ones, which is able totreat both types of outliers simultaneously, albeit with a certain delay. Wecheck our filters at a reference state space model, and compare the resultswith those obtained by the ACM filter Martin and Masreliez[1977], Martin[1979]and non-parametric, repeated-median based filters Fried et al.[2006,2007].

Optimally Robust Kalman Filtering at Work: AO-, IO-, and Simultaneously IO- and AO- Robust Filters